{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd.function import Function\n",
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from itertools import chain\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "import itertools\n",
    "import random\n",
    "import tqdm\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal\n",
    "from scipy.fft import fftshift\n",
    "\n",
    "from einops import rearrange, repeat\n",
    "from einops.layers.torch import Rearrange\n",
    "from torch import nn, einsum\n",
    "import math\n",
    "import logging\n",
    "from functools import partial\n",
    "from collections import OrderedDict\n",
    "from sklearn.metrics import classification_report\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sat Jun 19 13:26:43 2021       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 460.80       Driver Version: 460.80       CUDA Version: 11.2     |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                               |                      |               MIG M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  GeForce RTX 208...  Off  | 00000000:01:00.0 Off |                  N/A |\r\n",
      "| 54%   62C    P2   248W / 260W |   3126MiB / 11016MiB |     97%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                  |\r\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
      "|        ID   ID                                                   Usage      |\r\n",
      "|=============================================================================|\r\n",
      "|    0   N/A  N/A    100008      C   /opt/anaconda3/bin/python        3123MiB |\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!nvidia-smi\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(692382, 32, 30)\n",
      "(692382, 32, 30)\n"
     ]
    }
   ],
   "source": [
    "###Read the EEG Spectrogram###\n",
    "\n",
    "dat1 = np.load('/project/hikaku_db/data/sleep_SHHS/stages_sig/C4_spec_30_np/spec_1601_2308_30.npy')\n",
    "dat2 = np.load('/project/hikaku_db/data/sleep_SHHS/stages_sig/C3_spec_30_np/spec_c3_1601_2308_30.npy')\n",
    "print(dat1.shape)\n",
    "print(dat2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(692382, 2, 32, 30)\n",
      "(692382, 2, 20, 30)\n"
     ]
    }
   ],
   "source": [
    "dat = np.concatenate((dat1.reshape(-1,1,32,30), dat2.reshape(-1,1,32,30)), axis=1)\n",
    "print(dat.shape)        \n",
    "fixdata = dat[:,:,0:16,:]  \n",
    "mean_p1 = np.mean(dat[:,:,16:20,:], axis = 2)\n",
    "mean_p2 = np.mean(dat[:,:,20:24,:], axis = 2)\n",
    "mean_p3 = np.mean(dat[:,:,24:28,:], axis = 2)\n",
    "mean_p4 = np.mean(dat[:,:,28:32,:], axis = 2)\n",
    "num_data = len(dat)\n",
    "ch = 2\n",
    "inputdat = np.concatenate((fixdata,mean_p1.reshape(num_data, ch, 1, 30),mean_p2.reshape(num_data, ch, 1, 30),mean_p3.reshape(num_data, ch, 1, 30),mean_p4.reshape(num_data, ch, 1, 30)),axis=2)\n",
    "print(inputdat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        0\n",
      "2  283199\n",
      "0  208397\n",
      "4   88394\n",
      "3   87602\n",
      "1   24790\n",
      "          0\n",
      "0  0.118956\n",
      "1  1.000000\n",
      "2  0.087536\n",
      "3  0.282984\n",
      "4  0.280449\n"
     ]
    }
   ],
   "source": [
    "###Read the Lable###\n",
    "index = pd.read_csv(\"/project/hikaku_db/data/sleep_SHHS/stages_sig/ann_delrecords_5class.csv\", header=None)\n",
    "index = index[1594504 : 2286886].astype(int)\n",
    "print(index.apply(pd.value_counts))\n",
    "label = index.values.tolist()  #list\n",
    "weight = 1/(index.apply(pd.value_counts)/len(index))\n",
    "weight = (weight/weight.max()).sort_index()\n",
    "print(weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Mydatasets(torch.utils.data.Dataset):\n",
    "    def __init__(self, data,label ,transform = None):\n",
    "        self.transform = transform\n",
    "\n",
    "        self.data = data\n",
    "        self.label = label\n",
    "\n",
    "        self.datanum = len(data)\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.datanum\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        \n",
    "        out_data = torch.tensor(self.data[idx]).float()\n",
    "        out_label = torch.tensor(self.label[idx])\n",
    "        if self.transform:\n",
    "            out_data = self.transform(out_data)\n",
    "\n",
    "        return out_data, out_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data: 623143\n",
      "test data: 69239\n"
     ]
    }
   ],
   "source": [
    "train, test, train_label, test_label = train_test_split(inputdat, np.array(label),test_size = 0.1,stratify = label, random_state = 0)\n",
    "print('train data:',len(train))\n",
    "print('test data:',len(test))\n",
    "\n",
    "train_data_set = Mydatasets(data=train,label=train_label)\n",
    "test_data_set = Mydatasets(data=test,label=test_label)\n",
    "\n",
    "train_dataloader = torch.utils.data.DataLoader(train_data_set, batch_size = 64, shuffle=True)\n",
    "test_dataloader = torch.utils.data.DataLoader(test_data_set, batch_size = 64, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self,hidden_size,num_layers):\n",
    "        super().__init__()\n",
    "        self.to_patch = nn.Sequential(\n",
    "            #(batch, 4, 20, 30) -> (batch, 150, 16)  or  (batch, 30, 80)\n",
    "            Rearrange('b c (h p1) (w p2) -> b (h w p2) (c p1)', p1 = 4, p2 = 30),  \n",
    "        )\n",
    "        \n",
    "        self.lstm = nn.LSTM(\n",
    "            input_size = 8,\n",
    "            hidden_size = hidden_size,\n",
    "            num_layers = num_layers,\n",
    "            batch_first = True,\n",
    "            bidirectional =True\n",
    "        )\n",
    "        \n",
    "    def forward(self,x):\n",
    "        # output: [batch_size, time_step, hidden_size] ,same as input: [batch, seq_len, input_size](guide name)\n",
    "        # LSTM : batch_first = True, but the first dim of h_n and c_n will still be num_layers\n",
    "        # h_n and c_n: [num_layers,batch_size, hidden_size] \n",
    "        x = self.to_patch(x)\n",
    "        mean_out = True            #use mean_out of LSTM as output \n",
    "        output,(h_n,c_n)=self.lstm(x) \n",
    "        out = output[:,-1,:]     #output of the last timestep\n",
    "        if mean_out:\n",
    "            out = output.mean(dim = 1)      #mean of all timestep output\n",
    "        return out\n",
    "\n",
    "##LSTM model\n",
    "\n",
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self,hidden_size,num_layers):\n",
    "        super().__init__()     \n",
    "        self.lstm = LSTM(hidden_size,num_layers)\n",
    "    \n",
    "        self.to_latent = nn.Identity()\n",
    "        \n",
    "        self.mlp_head = nn.Sequential(\n",
    "            nn.LayerNorm(2 * hidden_size),\n",
    "            nn.Linear(2 * hidden_size, 5)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.lstm(x)        \n",
    "        x = self.to_latent(x)\n",
    "        return self.mlp_head(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(DEVICE)\n",
    "\n",
    "LSTMModel = LSTMModel(\n",
    "    hidden_size = 16,\n",
    "    num_layers = 8\n",
    ").to(DEVICE)\n",
    "\n",
    "#summary(Transmodel,(2286886, 20, 30))\n",
    "\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.AdamW(LSTMModel.parameters(), lr=1e-4)\n",
    "\n",
    "LSTMModel.load_state_dict(torch.load('/project/hikaku_db/ziwei/Model_12/Model_12_state_2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1 :finished\n",
      "train_loss: 0.47164125198779\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:40: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_loss: 0.4634641029092168\n",
      "{'0': {'precision': 0.8987388896644795, 'recall': 0.9267274472168906, 'f1-score': 0.9125186042665785, 'support': 20840}, '1': {'precision': 0.4521072796934866, 'recall': 0.04759983864461476, 'f1-score': 0.08613138686131386, 'support': 2479}, '2': {'precision': 0.8375320614173781, 'recall': 0.84170197740113, 'f1-score': 0.8396118419893979, 'support': 28320}, '3': {'precision': 0.8022488917720835, 'recall': 0.8470319634703196, 'f1-score': 0.8240324282303292, 'support': 8760}, '4': {'precision': 0.6942427651089068, 'recall': 0.7679864253393666, 'f1-score': 0.7292550620334067, 'support': 8840}, 'accuracy': 0.8301246407371567, 'macro avg': {'precision': 0.7369739775312669, 'recall': 0.6862095304144643, 'f1-score': 0.6783098646762051, 'support': 69239}, 'weighted avg': {'precision': 0.8193966799317473, 'recall': 0.8301246407371567, 'f1-score': 0.8185177949675391, 'support': 69239}}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 2 :finished\n",
      "train_loss: 0.45673907262240515\n",
      "val_loss: 0.4732775003158672\n",
      "{'0': {'precision': 0.8833544647708749, 'recall': 0.9360844529750479, 'f1-score': 0.9089553629671047, 'support': 20840}, '1': {'precision': 0.40032414910858993, 'recall': 0.09963695038321904, 'f1-score': 0.15956072351421188, 'support': 2479}, '2': {'precision': 0.8161106040631444, 'recall': 0.8525070621468926, 'f1-score': 0.8339118871215653, 'support': 28320}, '3': {'precision': 0.7700588059403967, 'recall': 0.8819634703196347, 'f1-score': 0.8222210397488426, 'support': 8760}, '4': {'precision': 0.792689973995955, 'recall': 0.6207013574660634, 'f1-score': 0.696231442710316, 'support': 8840}, 'accuracy': 0.8248386025217002, 'macro avg': {'precision': 0.7325075995757923, 'recall': 0.6781786586581715, 'f1-score': 0.6841760912124081, 'support': 69239}, 'weighted avg': {'precision': 0.8126468815074682, 'recall': 0.8248386025217002, 'f1-score': 0.813297530335042, 'support': 69239}}\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 3 :finished\n",
      "train_loss: 0.45091447533493084\n",
      "val_loss: 0.45234260471217513\n",
      "{'0': {'precision': 0.8899698547547273, 'recall': 0.9349808061420346, 'f1-score': 0.9119202508541208, 'support': 20840}, '1': {'precision': 0.4133611691022965, 'recall': 0.07987091569181122, 'f1-score': 0.13387423935091278, 'support': 2479}, '2': {'precision': 0.8187497933952597, 'recall': 0.8745762711864407, 'f1-score': 0.8457427737276126, 'support': 28320}, '3': {'precision': 0.8337201206776514, 'recall': 0.8202054794520548, 'f1-score': 0.8269075843019911, 'support': 8760}, '4': {'precision': 0.762160810303864, 'recall': 0.6894796380090498, 'f1-score': 0.724000712716042, 'support': 8840}, 'accuracy': 0.8337930934877742, 'macro avg': {'precision': 0.7435923496467598, 'recall': 0.6798226220962782, 'f1-score': 0.6884891121901358, 'support': 69239}, 'weighted avg': {'precision': 0.8203408206425461, 'recall': 0.8337930934877742, 'f1-score': 0.8222476401740642, 'support': 69239}}\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 4 :finished\n",
      "train_loss: 0.44661061732023666\n",
      "val_loss: 0.4468811322106892\n",
      "{'0': {'precision': 0.9085681968140258, 'recall': 0.9250479846449137, 'f1-score': 0.9167340339531124, 'support': 20840}, '1': {'precision': 0.4246575342465753, 'recall': 0.07503025413473174, 'f1-score': 0.12752828248200204, 'support': 2479}, '2': {'precision': 0.8223074358119373, 'recall': 0.8707980225988701, 'f1-score': 0.8458583433373349, 'support': 28320}, '3': {'precision': 0.8190793122573489, 'recall': 0.8429223744292238, 'f1-score': 0.8308298171589312, 'support': 8760}, '4': {'precision': 0.7411984145488458, 'recall': 0.7192307692307692, 'f1-score': 0.7300493742105867, 'support': 8840}, 'accuracy': 0.8357573044093647, 'macro avg': {'precision': 0.7431621787357465, 'recall': 0.6866058810077018, 'f1-score': 0.6901999702283934, 'support': 69239}, 'weighted avg': {'precision': 0.8232695820445384, 'recall': 0.8357573044093647, 'f1-score': 0.8247850753116437, 'support': 69239}}\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 5 :finished\n",
      "train_loss: 0.44358474757345207\n",
      "val_loss: 0.44732904120205513\n",
      "{'0': {'precision': 0.906588511137163, 'recall': 0.9276871401151632, 'f1-score': 0.9170164828649354, 'support': 20840}, '1': {'precision': 0.4939759036144578, 'recall': 0.03307785397337636, 'f1-score': 0.06200378071833649, 'support': 2479}, '2': {'precision': 0.8188453662725411, 'recall': 0.8754590395480226, 'f1-score': 0.8462063551657054, 'support': 28320}, '3': {'precision': 0.8351609480014149, 'recall': 0.8085616438356165, 'f1-score': 0.8216460762136768, 'support': 8760}, '4': {'precision': 0.730114584492157, 'recall': 0.742420814479638, 'f1-score': 0.7362162768523193, 'support': 8840}, 'accuracy': 0.8355695489536245, 'macro avg': {'precision': 0.7569370627035468, 'recall': 0.6774412983903633, 'f1-score': 0.6766177943629946, 'support': 69239}, 'weighted avg': {'precision': 0.8243590236918521, 'recall': 0.8355695489536245, 'f1-score': 0.8222918639582475, 'support': 69239}}\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 6 :finished\n",
      "train_loss: 0.44111365828228105\n",
      "val_loss: 0.444811813960234\n",
      "{'0': {'precision': 0.9277149708804385, 'recall': 0.9095969289827255, 'f1-score': 0.9185666174012065, 'support': 20840}, '1': {'precision': 0.4119170984455959, 'recall': 0.06413876563130294, 'f1-score': 0.11099476439790575, 'support': 2479}, '2': {'precision': 0.8206887408438567, 'recall': 0.8743290960451977, 'f1-score': 0.8466601699406745, 'support': 28320}, '3': {'precision': 0.8238521836506159, 'recall': 0.8398401826484019, 'f1-score': 0.8317693612210288, 'support': 8760}, '4': {'precision': 0.718102800729692, 'recall': 0.7570135746606335, 'f1-score': 0.7370449914642877, 'support': 8840}, 'accuracy': 0.8365949825965135, 'macro avg': {'precision': 0.7404551589100398, 'recall': 0.6889837095936523, 'f1-score': 0.6890071808850207, 'support': 69239}, 'weighted avg': {'precision': 0.8255694263077581, 'recall': 0.8365949825965135, 'f1-score': 0.8260846873748028, 'support': 69239}}\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 7 :finished\n",
      "train_loss: 0.4388076066588234\n",
      "val_loss: 0.4494739010682168\n",
      "{'0': {'precision': 0.8967866678943007, 'recall': 0.9347408829174664, 'f1-score': 0.9153705183027113, 'support': 20840}, '1': {'precision': 0.3778801843317972, 'recall': 0.13231141589350545, 'f1-score': 0.19599641469973114, 'support': 2479}, '2': {'precision': 0.8154738497837201, 'recall': 0.878707627118644, 'f1-score': 0.8459106669386091, 'support': 28320}, '3': {'precision': 0.826782449725777, 'recall': 0.826027397260274, 'f1-score': 0.8264047510278666, 'support': 8760}, '4': {'precision': 0.7882400758704783, 'recall': 0.6581447963800905, 'f1-score': 0.7173417175266631, 'support': 8840}, 'accuracy': 0.8340241771256084, 'macro avg': {'precision': 0.7410326455212147, 'recall': 0.685986423913996, 'f1-score': 0.7002048136991162, 'support': 69239}, 'weighted avg': {'precision': 0.8222342190390322, 'recall': 0.8340241771256084, 'f1-score': 0.8246651916277006, 'support': 69239}}\n"
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      "epoch 8 :finished\n",
      "train_loss: 0.4370239752083573\n",
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      "epoch 9 :finished\n",
      "train_loss: 0.4351541403751054\n",
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      "epoch 10 :finished\n",
      "train_loss: 0.43371649310214694\n",
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      "epoch 11 :finished\n",
      "train_loss: 0.43211814842118096\n",
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      "epoch 12 :finished\n",
      "train_loss: 0.4308055245351091\n",
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      "{'0': {'precision': 0.9098033694682998, 'recall': 0.9302783109404991, 'f1-score': 0.9199269259057155, 'support': 20840}, '1': {'precision': 0.43209876543209874, 'recall': 0.08471157724889068, 'f1-score': 0.14165261382799324, 'support': 2479}, '2': {'precision': 0.8410396622136084, 'recall': 0.8580861581920904, 'f1-score': 0.8494774006362078, 'support': 28320}, '3': {'precision': 0.8532244300865537, 'recall': 0.798972602739726, 'f1-score': 0.8252078052231326, 'support': 8760}, '4': {'precision': 0.6926645404465063, 'recall': 0.8107466063348416, 'f1-score': 0.7470683275134206, 'support': 8840}, 'accuracy': 0.8386025217001979, 'macro avg': {'precision': 0.7457661535294133, 'recall': 0.6965590510912095, 'f1-score': 0.6966666146212939, 'support': 69239}, 'weighted avg': {'precision': 0.8296930752642334, 'recall': 0.8386025217001979, 'f1-score': 0.8291936385641818, 'support': 69239}}\n"
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      "epoch 13 :finished\n",
      "train_loss: 0.4299167813973296\n",
      "val_loss: 0.4389816818693429\n",
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      "epoch 14 :finished\n",
      "train_loss: 0.4286440978372098\n",
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      "{'0': {'precision': 0.8914865660821893, 'recall': 0.9441458733205375, 'f1-score': 0.9170608934771971, 'support': 20840}, '1': {'precision': 0.4282238442822384, 'recall': 0.07099636950383219, 'f1-score': 0.1217993079584775, 'support': 2479}, '2': {'precision': 0.8570341378560556, 'recall': 0.8350635593220339, 'f1-score': 0.8459062131129951, 'support': 28320}, '3': {'precision': 0.8562720955747897, 'recall': 0.8018264840182648, 'f1-score': 0.8281553970406178, 'support': 8760}, '4': {'precision': 0.668978102189781, 'recall': 0.8294117647058824, 'f1-score': 0.7406060606060607, 'support': 8840}, 'accuracy': 0.8356128771357183, 'macro avg': {'precision': 0.7403989491970109, 'recall': 0.6962888101741103, 'f1-score': 0.6907055744390697, 'support': 69239}, 'weighted avg': {'precision': 0.8279447090773238, 'recall': 0.8356128771357183, 'f1-score': 0.8257073659886373, 'support': 69239}}\n"
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      "epoch 15 :finished\n",
      "train_loss: 0.42763458358852163\n",
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      "epoch 16 :finished\n",
      "train_loss: 0.426744621764699\n",
      "val_loss: 0.43384753157988937\n",
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      "epoch 17 :finished\n",
      "train_loss: 0.4252222557264737\n",
      "val_loss: 0.43586338447775286\n",
      "{'0': {'precision': 0.8930671407083276, 'recall': 0.9401631477927064, 'f1-score': 0.9160101919165946, 'support': 20840}, '1': {'precision': 0.43544857768052514, 'recall': 0.08027430415490117, 'f1-score': 0.13555858310626706, 'support': 2479}, '2': {'precision': 0.840012415505587, 'recall': 0.8600635593220339, 'f1-score': 0.8499197431781703, 'support': 28320}, '3': {'precision': 0.8446144791053561, 'recall': 0.8190639269406392, 'f1-score': 0.8316430020283976, 'support': 8760}, '4': {'precision': 0.7271171941830624, 'recall': 0.7692307692307693, 'f1-score': 0.7475813544415129, 'support': 8840}, 'accuracy': 0.839469085342076, 'macro avg': {'precision': 0.7480519614365717, 'recall': 0.6937591414882099, 'f1-score': 0.6961425749341885, 'support': 69239}, 'weighted avg': {'precision': 0.8276648518478169, 'recall': 0.839469085342076, 'f1-score': 0.8288571632302574, 'support': 69239}}\n"
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      "epoch 18 :finished\n",
      "train_loss: 0.4245187047479872\n",
      "val_loss: 0.4347001854709028\n",
      "{'0': {'precision': 0.9140883716529473, 'recall': 0.9271593090211132, 'f1-score': 0.9205774453285054, 'support': 20840}, '1': {'precision': 0.4641509433962264, 'recall': 0.04961678096006454, 'f1-score': 0.08965014577259475, 'support': 2479}, '2': {'precision': 0.8160377358490566, 'recall': 0.8857697740112994, 'f1-score': 0.8494751100575686, 'support': 28320}, '3': {'precision': 0.8403478260869566, 'recall': 0.8273972602739726, 'f1-score': 0.8338222605694565, 'support': 8760}, '4': {'precision': 0.7543383307755873, 'recall': 0.7228506787330317, 'f1-score': 0.738258910519323, 'support': 8840}, 'accuracy': 0.84010456534612, 'macro avg': {'precision': 0.7577926415521548, 'recall': 0.6825587605998963, 'f1-score': 0.6863567744494896, 'support': 69239}, 'weighted avg': {'precision': 0.8281491404230258, 'recall': 0.84010456534612, 'f1-score': 0.8274917829608445, 'support': 69239}}\n"
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      "epoch 19 :finished\n",
      "train_loss: 0.4236031967358042\n",
      "val_loss: 0.4345024701848268\n",
      "{'0': {'precision': 0.9075732139526208, 'recall': 0.9338771593090212, 'f1-score': 0.9205373190805032, 'support': 20840}, '1': {'precision': 0.42247191011235957, 'recall': 0.07583703106091166, 'f1-score': 0.12859097127222982, 'support': 2479}, '2': {'precision': 0.8506447498937225, 'recall': 0.8478813559322034, 'f1-score': 0.8492608049798401, 'support': 28320}, '3': {'precision': 0.8142076502732241, 'recall': 0.8504566210045662, 'f1-score': 0.8319374651032942, 'support': 8760}, '4': {'precision': 0.7051744885679904, 'recall': 0.7954751131221719, 'f1-score': 0.7476079098447799, 'support': 8840}, 'accuracy': 0.8397579398893688, 'macro avg': {'precision': 0.7400144025599835, 'recall': 0.7007054560857748, 'f1-score': 0.6955868940561294, 'support': 69239}, 'weighted avg': {'precision': 0.8292666626650567, 'recall': 0.8397579398893688, 'f1-score': 0.8297414298557653, 'support': 69239}}\n"
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      "epoch 20 :finished\n",
      "train_loss: 0.422703655639217\n",
      "val_loss: 0.4322456245942389\n",
      "{'0': {'precision': 0.9101973684210526, 'recall': 0.9294145873320537, 'f1-score': 0.9197056030389363, 'support': 20840}, '1': {'precision': 0.3943262411347518, 'recall': 0.11214199273900767, 'f1-score': 0.17462311557788945, 'support': 2479}, '2': {'precision': 0.8437521730060497, 'recall': 0.8569209039548022, 'f1-score': 0.8502855541151325, 'support': 28320}, '3': {'precision': 0.8231171431749916, 'recall': 0.844634703196347, 'f1-score': 0.8337371119499689, 'support': 8760}, '4': {'precision': 0.7256655792907503, 'recall': 0.7800904977375566, 'f1-score': 0.7518944556506569, 'support': 8840}, 'accuracy': 0.8407111598954347, 'macro avg': {'precision': 0.7394117010055192, 'recall': 0.7046405369919534, 'f1-score': 0.7060491680665169, 'support': 69239}, 'weighted avg': {'precision': 0.8299729826303422, 'recall': 0.8407111598954347, 'f1-score': 0.8323333157905677, 'support': 69239}}\n"
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      "epoch 21 :finished\n",
      "train_loss: 0.42194509874125263\n",
      "val_loss: 0.4408118445942671\n",
      "{'0': {'precision': 0.9252004860267314, 'recall': 0.9134357005758157, 'f1-score': 0.9192804539418085, 'support': 20840}, '1': {'precision': 0.4031620553359684, 'recall': 0.041145623235175476, 'f1-score': 0.07467057101024892, 'support': 2479}, '2': {'precision': 0.7904444376472027, 'recall': 0.9125, 'f1-score': 0.8470981594086506, 'support': 28320}, '3': {'precision': 0.8815580286168522, 'recall': 0.7595890410958904, 'f1-score': 0.8160412067696836, 'support': 8760}, '4': {'precision': 0.7670746634026928, 'recall': 0.7089366515837104, 'f1-score': 0.7368606701940035, 'support': 8840}, 'accuracy': 0.8362483571397623, 'macro avg': {'precision': 0.7534879342058896, 'recall': 0.6671214032981184, 'f1-score': 0.678790212264879, 'support': 69239}, 'weighted avg': {'precision': 0.825681937828495, 'recall': 0.8362483571397623, 'f1-score': 0.823164721846822, 'support': 69239}}\n"
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      "epoch 22 :finished\n",
      "train_loss: 0.421756260941023\n",
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      "{'0': {'precision': 0.8996550931248563, 'recall': 0.9387236084452975, 'f1-score': 0.9187742162733357, 'support': 20840}, '1': {'precision': 0.4491017964071856, 'recall': 0.09076240419524001, 'f1-score': 0.15100671140939595, 'support': 2479}, '2': {'precision': 0.8214040255277368, 'recall': 0.886228813559322, 'f1-score': 0.8525859872611465, 'support': 28320}, '3': {'precision': 0.8562202997441208, 'recall': 0.8021689497716895, 'f1-score': 0.8283137855837802, 'support': 8760}, '4': {'precision': 0.7658850686429353, 'recall': 0.7131221719457014, 'f1-score': 0.7385624743717416, 'support': 8840}, 'accuracy': 0.8408122589869871, 'macro avg': {'precision': 0.758453256689367, 'recall': 0.6862011895834501, 'f1-score': 0.69784863497988, 'support': 69239}, 'weighted avg': {'precision': 0.8289433892679412, 'recall': 0.8408122589869871, 'f1-score': 0.8297600557361601, 'support': 69239}}\n"
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      "epoch 23 :finished\n",
      "train_loss: 0.4202807630459435\n",
      "val_loss: 0.43292122611056416\n",
      "{'0': {'precision': 0.9166429317383461, 'recall': 0.9265834932821497, 'f1-score': 0.9215864076743188, 'support': 20840}, '1': {'precision': 0.5078125, 'recall': 0.05244050020169423, 'f1-score': 0.09506398537477148, 'support': 2479}, '2': {'precision': 0.8303726459195939, 'recall': 0.8781073446327684, 'f1-score': 0.8535731447792956, 'support': 28320}, '3': {'precision': 0.8513175152020985, 'recall': 0.815068493150685, 'f1-score': 0.8327987403044265, 'support': 8760}, '4': {'precision': 0.7135253600500939, 'recall': 0.7734162895927602, 'f1-score': 0.7422646835305614, 'support': 8840}, 'accuracy': 0.8417943644477823, 'macro avg': {'precision': 0.7639341905820265, 'recall': 0.6891232241720114, 'f1-score': 0.6890573923326747, 'support': 69239}, 'weighted avg': {'precision': 0.8325216255778282, 'recall': 0.8417943644477823, 'f1-score': 0.8300474094557034, 'support': 69239}}\n"
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      "epoch 24 :finished\n",
      "train_loss: 0.41982341597913236\n",
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      "{'0': {'precision': 0.8819432041435971, 'recall': 0.9477927063339732, 'f1-score': 0.9136830419095199, 'support': 20840}, '1': {'precision': 0.40472175379426645, 'recall': 0.09681323114158935, 'f1-score': 0.15625, 'support': 2479}, '2': {'precision': 0.8348923432769823, 'recall': 0.86670197740113, 'f1-score': 0.8504998354094839, 'support': 28320}, '3': {'precision': 0.8755213764337852, 'recall': 0.7667808219178082, 'f1-score': 0.8175511197663097, 'support': 8760}, '4': {'precision': 0.7298180629698224, 'recall': 0.7578054298642534, 'f1-score': 0.7435484766080248, 'support': 8840}, 'accuracy': 0.8369993789627234, 'macro avg': {'precision': 0.7453793481236907, 'recall': 0.6871788333317508, 'f1-score': 0.6963064947386677, 'support': 69239}, 'weighted avg': {'precision': 0.825377485200911, 'recall': 0.8369993789627234, 'f1-score': 0.8268370430618407, 'support': 69239}}\n"
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      "epoch 25 :finished\n",
      "train_loss: 0.4189173171404415\n",
      "val_loss: 0.4322297490320673\n",
      "{'0': {'precision': 0.9094823551436862, 'recall': 0.9324376199616122, 'f1-score': 0.9208169454579916, 'support': 20840}, '1': {'precision': 0.40182054616384916, 'recall': 0.12464703509479629, 'f1-score': 0.1902709359605911, 'support': 2479}, '2': {'precision': 0.8426057682296185, 'recall': 0.857274011299435, 'f1-score': 0.8498766037141408, 'support': 28320}, '3': {'precision': 0.8136052945643919, 'recall': 0.8560502283105023, 'f1-score': 0.8342882572175557, 'support': 8760}, '4': {'precision': 0.7406876790830945, 'recall': 0.7602941176470588, 'f1-score': 0.7503628447024673, 'support': 8840}, 'accuracy': 0.8411299989890091, 'macro avg': {'precision': 0.7416403286369281, 'recall': 0.706140602462681, 'f1-score': 0.7091231174105493, 'support': 69239}, 'weighted avg': {'precision': 0.8302716999794341, 'recall': 0.8411299989890091, 'f1-score': 0.8329349772696156, 'support': 69239}}\n"
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      "epoch 26 :finished\n",
      "train_loss: 0.4184683918420375\n",
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      "{'0': {'precision': 0.9260387945629566, 'recall': 0.9186180422264876, 'f1-score': 0.9223134921590829, 'support': 20840}, '1': {'precision': 0.449438202247191, 'recall': 0.08067769261799113, 'f1-score': 0.13679890560875516, 'support': 2479}, '2': {'precision': 0.8266193257233874, 'recall': 0.8796257062146893, 'f1-score': 0.8522991651840701, 'support': 28320}, '3': {'precision': 0.8681944796150924, 'recall': 0.7827625570776255, 'f1-score': 0.8232680994116941, 'support': 8760}, '4': {'precision': 0.7021909388321602, 'recall': 0.8012443438914028, 'f1-score': 0.7484545886828341, 'support': 8840}, 'accuracy': 0.8404945189849651, 'macro avg': {'precision': 0.7544963481961575, 'recall': 0.6925856684056393, 'f1-score': 0.6966268502092873, 'support': 69239}, 'weighted avg': {'precision': 0.8324126088946062, 'recall': 0.8404945189849651, 'f1-score': 0.8308239162381743, 'support': 69239}}\n"
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      "epoch 27 :finished\n",
      "train_loss: 0.41761340398930225\n",
      "val_loss: 0.4309047947253846\n",
      "{'0': {'precision': 0.9058210413661533, 'recall': 0.9341170825335893, 'f1-score': 0.9197514823651697, 'support': 20840}, '1': {'precision': 0.4720496894409938, 'recall': 0.06131504638967326, 'f1-score': 0.10853266690467692, 'support': 2479}, '2': {'precision': 0.8304682274247491, 'recall': 0.8768008474576271, 'f1-score': 0.8530058399175541, 'support': 28320}, '3': {'precision': 0.8443191164375514, 'recall': 0.8203196347031964, 'f1-score': 0.83214637253199, 'support': 8760}, '4': {'precision': 0.7402107598447033, 'recall': 0.7548642533936651, 'f1-score': 0.747465695883506, 'support': 8840}, 'accuracy': 0.8421409899045336, 'macro avg': {'precision': 0.7585737669028302, 'recall': 0.6894833728955503, 'f1-score': 0.6921804115205793, 'support': 69239}, 'weighted avg': {'precision': 0.830544641890898, 'recall': 0.8421409899045336, 'f1-score': 0.8303268062104072, 'support': 69239}}\n"
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      "epoch 28 :finished\n",
      "train_loss: 0.41673187387819666\n",
      "val_loss: 0.43438963607201514\n",
      "{'0': {'precision': 0.8858193559960534, 'recall': 0.9477927063339732, 'f1-score': 0.9157587278037925, 'support': 20840}, '1': {'precision': 0.4391304347826087, 'recall': 0.08148446954417103, 'f1-score': 0.13746172167403878, 'support': 2479}, '2': {'precision': 0.8361497398932373, 'recall': 0.868361581920904, 'f1-score': 0.8519512913339453, 'support': 28320}, '3': {'precision': 0.8497663831316641, 'recall': 0.8097031963470319, 'f1-score': 0.8292511837259601, 'support': 8760}, '4': {'precision': 0.7489395850051588, 'recall': 0.739027149321267, 'f1-score': 0.7439503501679668, 'support': 8840}, 'accuracy': 0.8401623362555785, 'macro avg': {'precision': 0.7519610997617445, 'recall': 0.6892738206934694, 'f1-score': 0.6956746549411407, 'support': 69239}, 'weighted avg': {'precision': 0.8274732420780095, 'recall': 0.8401623362555785, 'f1-score': 0.828914362295275, 'support': 69239}}\n"
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      "epoch 29 :finished\n",
      "train_loss: 0.41646503245051886\n",
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      "epoch 30 :finished\n",
      "train_loss: 0.4160327660795035\n",
      "val_loss: 0.43452783672624506\n",
      "{'0': {'precision': 0.8841209406494961, 'recall': 0.9471209213051823, 'f1-score': 0.9145372408201089, 'support': 20840}, '1': {'precision': 0.4389312977099237, 'recall': 0.0927793465106898, 'f1-score': 0.15318015318015318, 'support': 2479}, '2': {'precision': 0.8457932567103025, 'recall': 0.8600988700564972, 'f1-score': 0.8528860799383743, 'support': 28320}, '3': {'precision': 0.8588522588522588, 'recall': 0.8029680365296804, 'f1-score': 0.8299705014749262, 'support': 8760}, '4': {'precision': 0.727475800446761, 'recall': 0.7736425339366516, 'f1-score': 0.7498492407214518, 'support': 8840}, 'accuracy': 0.8405522898944237, 'macro avg': {'precision': 0.7510347108737484, 'recall': 0.6953219416677403, 'f1-score': 0.7000846432270029, 'support': 69239}, 'weighted avg': {'precision': 0.8293084530927559, 'recall': 0.8405522898944237, 'f1-score': 0.8303359719692285, 'support': 69239}}\n"
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      "epoch 31 :finished\n",
      "train_loss: 0.4151955948475145\n",
      "val_loss: 0.43474603305241977\n",
      "{'0': {'precision': 0.8948784603456925, 'recall': 0.9415547024952016, 'f1-score': 0.9176234012205673, 'support': 20840}, '1': {'precision': 0.41374045801526715, 'recall': 0.10931827349737798, 'f1-score': 0.17294192724952137, 'support': 2479}, '2': {'precision': 0.8262022337851722, 'recall': 0.8802612994350283, 'f1-score': 0.8523754979228284, 'support': 28320}, '3': {'precision': 0.864406779661017, 'recall': 0.7917808219178082, 'f1-score': 0.8265014299332698, 'support': 8760}, '4': {'precision': 0.7563829787234042, 'recall': 0.723868778280543, 'f1-score': 0.7397687861271676, 'support': 8840}, 'accuracy': 0.839945695345109, 'macro avg': {'precision': 0.7511221821061106, 'recall': 0.6893567751251918, 'f1-score': 0.7018422084906708, 'support': 69239}, 'weighted avg': {'precision': 0.8280247532686139, 'recall': 0.839945695345109, 'f1-score': 0.8300376581961365, 'support': 69239}}\n"
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      "epoch 32 :finished\n",
      "train_loss: 0.4147376798822538\n",
      "val_loss: 0.4300404078225552\n",
      "{'0': {'precision': 0.9120030148859997, 'recall': 0.9289827255278311, 'f1-score': 0.9204145668916992, 'support': 20840}, '1': {'precision': 0.39438700147710487, 'recall': 0.10770471964501815, 'f1-score': 0.16920152091254753, 'support': 2479}, '2': {'precision': 0.8137636205843592, 'recall': 0.8939618644067797, 'f1-score': 0.8519796066026148, 'support': 28320}, '3': {'precision': 0.8563428293158023, 'recall': 0.8029680365296804, 'f1-score': 0.8287969836220102, 'support': 8760}, '4': {'precision': 0.7801223623423649, 'recall': 0.7067873303167421, 'f1-score': 0.7416463885097039, 'support': 8840}, 'accuracy': 0.8409422435332689, 'macro avg': {'precision': 0.7513237657211262, 'recall': 0.6880809352852103, 'f1-score': 0.7024078133077151, 'support': 69239}, 'weighted avg': {'precision': 0.8294091308330273, 'recall': 0.8409422435332689, 'f1-score': 0.8311120647944923, 'support': 69239}}\n"
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      "epoch 33 :finished\n",
      "train_loss: 0.413981685129306\n",
      "val_loss: 0.42528677362666334\n",
      "{'0': {'precision': 0.9232768579129765, 'recall': 0.9204414587332054, 'f1-score': 0.9218569780853518, 'support': 20840}, '1': {'precision': 0.4328097731239092, 'recall': 0.100040338846309, 'f1-score': 0.16251638269986893, 'support': 2479}, '2': {'precision': 0.8310741049125728, 'recall': 0.8811087570621469, 'f1-score': 0.8553603565001285, 'support': 28320}, '3': {'precision': 0.8562469850458273, 'recall': 0.8105022831050228, 'f1-score': 0.8327468918601922, 'support': 8760}, '4': {'precision': 0.7242243810717643, 'recall': 0.7842760180995475, 'f1-score': 0.7530549068592843, 'support': 8840}, 'accuracy': 0.8436863617325496, 'macro avg': {'precision': 0.75352642041341, 'recall': 0.6992737711692463, 'f1-score': 0.7051071032009651, 'support': 69239}, 'weighted avg': {'precision': 0.8341095468634803, 'recall': 0.8436863617325496, 'f1-score': 0.8346459507131343, 'support': 69239}}\n"
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      "epoch 34 :finished\n",
      "train_loss: 0.41342142714334607\n",
      "val_loss: 0.437266512410698\n",
      "{'0': {'precision': 0.894403427217209, 'recall': 0.9416986564299424, 'f1-score': 0.9174419148239914, 'support': 20840}, '1': {'precision': 0.37037037037037035, 'recall': 0.14118596208148446, 'f1-score': 0.20443925233644858, 'support': 2479}, '2': {'precision': 0.833996530966228, 'recall': 0.8658898305084746, 'f1-score': 0.8496439893976405, 'support': 28320}, '3': {'precision': 0.8216883403129821, 'recall': 0.8511415525114155, 'f1-score': 0.8361556577324212, 'support': 8760}, '4': {'precision': 0.7813333333333333, 'recall': 0.6960407239819004, 'f1-score': 0.7362249476518098, 'support': 8840}, 'accuracy': 0.8392091162495126, 'macro avg': {'precision': 0.7403584004400245, 'recall': 0.6991913451026435, 'f1-score': 0.7087811523884623, 'support': 69239}, 'weighted avg': {'precision': 0.8272978214030641, 'recall': 0.8392091162495126, 'f1-score': 0.8307624935396704, 'support': 69239}}\n"
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      "epoch 35 :finished\n",
      "train_loss: 0.41300164018034063\n",
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      "epoch 36 :finished\n",
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      "epoch 37 :finished\n",
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      "epoch 38 :finished\n",
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      "epoch 39 :finished\n",
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      "epoch 40 :finished\n",
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      "epoch 41 :finished\n",
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      "epoch 42 :finished\n",
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      "epoch 43 :finished\n",
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      "epoch 44 :finished\n",
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      "epoch 45 :finished\n",
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      "epoch 46 :finished\n",
      "train_loss: 0.40810858699236485\n",
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      "epoch 47 :finished\n",
      "train_loss: 0.4076472754667975\n",
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      "epoch 48 :finished\n",
      "train_loss: 0.4076427873080548\n",
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      "epoch 49 :finished\n",
      "train_loss: 0.4073307028376396\n",
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      "{'0': {'precision': 0.924750943031241, 'recall': 0.9175623800383877, 'f1-score': 0.9211426369285611, 'support': 20840}, '1': {'precision': 0.4182590233545648, 'recall': 0.07946752722872126, 'f1-score': 0.1335593220338983, 'support': 2479}, '2': {'precision': 0.8264350252458145, 'recall': 0.8784957627118644, 'f1-score': 0.8516705463508147, 'support': 28320}, '3': {'precision': 0.8469399881164587, 'recall': 0.8135844748858447, 'f1-score': 0.8299272197962154, 'support': 8760}, '4': {'precision': 0.7197784975446662, 'recall': 0.7792986425339367, 'f1-score': 0.7483569605127369, 'support': 8840}, 'accuracy': 0.8407689308048932, 'macro avg': {'precision': 0.7472326954585491, 'recall': 0.6936817574797509, 'f1-score': 0.6969313371244453, 'support': 69239}, 'weighted avg': {'precision': 0.830389663351919, 'recall': 0.8407689308048932, 'f1-score': 0.8309284357358679, 'support': 69239}}\n"
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      "epoch 50 :finished\n",
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      "epoch 51 :finished\n",
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      "epoch 52 :finished\n",
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      "epoch 53 :finished\n",
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      "epoch 54 :finished\n",
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      "epoch 55 :finished\n",
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      "epoch 56 :finished\n",
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      "epoch 57 :finished\n",
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      "epoch 58 :finished\n",
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      "epoch 59 :finished\n",
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      "epoch 60 :finished\n",
      "train_loss: 0.403675254075094\n",
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      "epoch 61 :finished\n",
      "train_loss: 0.4032682466827722\n",
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      "epoch 62 :finished\n",
      "train_loss: 0.40272447835285585\n",
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      "epoch 63 :finished\n",
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      "epoch 64 :finished\n",
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      "epoch 65 :finished\n",
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      "epoch 66 :finished\n",
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      "epoch 67 :finished\n",
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      "epoch 68 :finished\n",
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      "epoch 69 :finished\n",
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      "epoch 70 :finished\n",
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      "epoch 71 :finished\n",
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      "epoch 72 :finished\n",
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      "epoch 73 :finished\n",
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      "epoch 74 :finished\n",
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      "epoch 75 :finished\n",
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      "epoch 76 :finished\n",
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      "epoch 77 :finished\n",
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      "epoch 78 :finished\n",
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      "epoch 79 :finished\n",
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      "epoch 80 :finished\n",
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      "epoch 81 :finished\n",
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      "epoch 82 :finished\n",
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      "epoch 83 :finished\n",
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      "epoch 84 :finished\n",
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      "epoch 85 :finished\n",
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      "epoch 86 :finished\n",
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      "epoch 87 :finished\n",
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      "epoch 88 :finished\n",
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      "epoch 89 :finished\n",
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      "epoch 90 :finished\n",
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      "epoch 91 :finished\n",
      "train_loss: 0.3962541482550684\n",
      "val_loss: 0.42214334009700694\n",
      "{'0': {'precision': 0.90905276897259, 'recall': 0.9357485604606526, 'f1-score': 0.9222075096945049, 'support': 20840}, '1': {'precision': 0.3907766990291262, 'recall': 0.12989108511496572, 'f1-score': 0.1949742658189525, 'support': 2479}, '2': {'precision': 0.8314445703619758, 'recall': 0.8808262711864406, 'f1-score': 0.8554233393916532, 'support': 28320}, '3': {'precision': 0.836498010233087, 'recall': 0.8398401826484019, 'f1-score': 0.8381657647393905, 'support': 8760}, '4': {'precision': 0.7793289248101886, 'recall': 0.7199095022624434, 'f1-score': 0.7484417264494884, 'support': 8840}, 'accuracy': 0.844740680830168, 'macro avg': {'precision': 0.7494201946813935, 'recall': 0.7012431203345809, 'f1-score': 0.7118425212187979, 'support': 69239}, 'weighted avg': {'precision': 0.8330116789655692, 'recall': 0.844740680830168, 'f1-score': 0.8360359282990919, 'support': 69239}}\n"
     ]
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    {
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     "text": [
      "epoch 92 :finished\n",
      "train_loss: 0.39584254104859457\n",
      "val_loss: 0.42871433215529114\n",
      "{'0': {'precision': 0.904223786587625, 'recall': 0.936852207293666, 'f1-score': 0.9202488687782805, 'support': 20840}, '1': {'precision': 0.36259541984732824, 'recall': 0.15328761597418314, 'f1-score': 0.2154805783952367, 'support': 2479}, '2': {'precision': 0.8198606836222259, 'recall': 0.8935381355932204, 'f1-score': 0.8551153163808397, 'support': 28320}, '3': {'precision': 0.8762820512820513, 'recall': 0.7802511415525114, 'f1-score': 0.8254830917874396, 'support': 8760}, '4': {'precision': 0.7861104108898412, 'recall': 0.7055429864253394, 'f1-score': 0.7436508882794801, 'support': 8840}, 'accuracy': 0.8417365935383238, 'macro avg': {'precision': 0.7498144704458143, 'recall': 0.6938944173677841, 'f1-score': 0.7119957487242553, 'support': 69239}, 'weighted avg': {'precision': 0.8317104394917031, 'recall': 0.8417365935383238, 'f1-score': 0.8338383609748135, 'support': 69239}}\n"
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      "epoch 93 :finished\n",
      "train_loss: 0.3958986357498722\n",
      "val_loss: 0.42904516145614513\n",
      "{'0': {'precision': 0.8814890407312095, 'recall': 0.9533109404990403, 'f1-score': 0.9159942828161741, 'support': 20840}, '1': {'precision': 0.3958041958041958, 'recall': 0.11415893505445744, 'f1-score': 0.17720726361928618, 'support': 2479}, '2': {'precision': 0.8426542235641254, 'recall': 0.867231638418079, 'f1-score': 0.8547662965927678, 'support': 28320}, '3': {'precision': 0.8520639465521356, 'recall': 0.8152968036529681, 'f1-score': 0.8332749970831875, 'support': 8760}, '4': {'precision': 0.7695672735871364, 'recall': 0.7363122171945702, 'f1-score': 0.7525725517400855, 'support': 8840}, 'accuracy': 0.8428920117274946, 'macro avg': {'precision': 0.7483157360477606, 'recall': 0.6972621069638231, 'f1-score': 0.7067630783703003, 'support': 69239}, 'weighted avg': {'precision': 0.8302033924793836, 'recall': 0.8428920117274946, 'f1-score': 0.8331695938956303, 'support': 69239}}\n"
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      "epoch 94 :finished\n",
      "train_loss: 0.39562139925867845\n",
      "val_loss: 0.4197322836837795\n",
      "{'0': {'precision': 0.9232056615502319, 'recall': 0.9264395393474089, 'f1-score': 0.9248197734294542, 'support': 20840}, '1': {'precision': 0.3897795591182365, 'recall': 0.15691811214199275, 'f1-score': 0.22375611159045156, 'support': 2479}, '2': {'precision': 0.8330661858011087, 'recall': 0.8808968926553672, 'f1-score': 0.856314145470772, 'support': 28320}, '3': {'precision': 0.8599169516365412, 'recall': 0.8037671232876712, 'f1-score': 0.8308945008260561, 'support': 8760}, '4': {'precision': 0.7465738525125082, 'recall': 0.7764705882352941, 'f1-score': 0.761228790063214, 'support': 8840}, 'accuracy': 0.8455928017446814, 'macro avg': {'precision': 0.7505084421237254, 'recall': 0.7088984511335468, 'f1-score': 0.7194026642759895, 'support': 69239}, 'weighted avg': {'precision': 0.8366800394025767, 'recall': 0.8455928017446814, 'f1-score': 0.8389296553969566, 'support': 69239}}\n"
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      "epoch 95 :finished\n",
      "train_loss: 0.3959981849459367\n",
      "val_loss: 0.4272735213065544\n",
      "{'0': {'precision': 0.9263599537037037, 'recall': 0.9217370441458733, 'f1-score': 0.9240427169520877, 'support': 20840}, '1': {'precision': 0.45475113122171945, 'recall': 0.08108108108108109, 'f1-score': 0.13762410133515918, 'support': 2479}, '2': {'precision': 0.8482632541133455, 'recall': 0.8519774011299435, 'f1-score': 0.8501162708759072, 'support': 28320}, '3': {'precision': 0.8328969834945931, 'recall': 0.8352739726027397, 'f1-score': 0.834083784554004, 'support': 8760}, '4': {'precision': 0.6826994091580503, 'recall': 0.8365384615384616, 'f1-score': 0.7518300122000815, 'support': 8840}, 'accuracy': 0.8412888689900201, 'macro avg': {'precision': 0.7489941463382823, 'recall': 0.7053215920996199, 'f1-score': 0.6995393771834479, 'support': 69239}, 'weighted avg': {'precision': 0.8345979173347907, 'recall': 0.8412888689900201, 'f1-score': 0.8322804260639071, 'support': 69239}}\n"
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      "epoch 96 :finished\n",
      "train_loss: 0.39517840776732904\n",
      "val_loss: 0.4289641503568277\n",
      "{'0': {'precision': 0.9199923711438516, 'recall': 0.9258637236084453, 'f1-score': 0.9229187094922632, 'support': 20840}, '1': {'precision': 0.39355581127733025, 'recall': 0.13795885437676483, 'f1-score': 0.2043010752688172, 'support': 2479}, '2': {'precision': 0.8115223197252956, 'recall': 0.9012711864406779, 'f1-score': 0.8540453724151776, 'support': 28320}, '3': {'precision': 0.8958217270194986, 'recall': 0.7342465753424657, 'f1-score': 0.80702634880803, 'support': 8760}, '4': {'precision': 0.7557330290929835, 'recall': 0.7493212669683258, 'f1-score': 0.7525134904856575, 'support': 8840}, 'accuracy': 0.8408122589869871, 'macro avg': {'precision': 0.7553250516517919, 'recall': 0.6897323213473359, 'f1-score': 0.708160999293989, 'support': 69239}, 'weighted avg': {'precision': 0.8327482527374388, 'recall': 0.8408122589869871, 'f1-score': 0.8326004605736587, 'support': 69239}}\n"
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      "epoch 97 :finished\n",
      "train_loss: 0.3950689168545736\n",
      "val_loss: 0.4202912843023563\n",
      "{'0': {'precision': 0.9244569897617453, 'recall': 0.9272072936660268, 'f1-score': 0.9258300991806813, 'support': 20840}, '1': {'precision': 0.4192495921696574, 'recall': 0.1036708350141186, 'f1-score': 0.16623544631306597, 'support': 2479}, '2': {'precision': 0.8195183286584776, 'recall': 0.8975635593220339, 'f1-score': 0.856767278426614, 'support': 28320}, '3': {'precision': 0.8710569310041473, 'recall': 0.7912100456621004, 'f1-score': 0.829215768379494, 'support': 8760}, '4': {'precision': 0.7604571428571428, 'recall': 0.7527149321266968, 'f1-score': 0.756566230812962, 'support': 8840}, 'accuracy': 0.8461127399298084, 'macro avg': {'precision': 0.7589477968902341, 'recall': 0.6944733331581954, 'f1-score': 0.7069229646225634, 'support': 69239}, 'weighted avg': {'precision': 0.8357524275579502, 'recall': 0.8461127399298084, 'f1-score': 0.8365519703457321, 'support': 69239}}\n"
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     "text": [
      "epoch 98 :finished\n",
      "train_loss: 0.3947121691392214\n",
      "val_loss: 0.4215151075542304\n",
      "{'0': {'precision': 0.9313620420888542, 'recall': 0.9174184261036469, 'f1-score': 0.9243376522916265, 'support': 20840}, '1': {'precision': 0.43470149253731344, 'recall': 0.09398951189995966, 'f1-score': 0.15456053067993367, 'support': 2479}, '2': {'precision': 0.8324929036567039, 'recall': 0.8802612994350283, 'f1-score': 0.8557109757144082, 'support': 28320}, '3': {'precision': 0.8422020457418687, 'recall': 0.8365296803652968, 'f1-score': 0.839356279709066, 'support': 8760}, '4': {'precision': 0.7272536467625145, 'recall': 0.7839366515837104, 'f1-score': 0.75453209211171, 'support': 8840}, 'accuracy': 0.8454628171983998, 'macro avg': {'precision': 0.7536024261574509, 'recall': 0.7024271138775283, 'f1-score': 0.7056995061013489, 'support': 69239}, 'weighted avg': {'precision': 0.8358009380084775, 'recall': 0.8454628171983998, 'f1-score': 0.8362759682558101, 'support': 69239}}\n"
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     "text": [
      "epoch 99 :finished\n",
      "train_loss: 0.39460080478382853\n",
      "val_loss: 0.4207370478157195\n",
      "{'0': {'precision': 0.9075131096570607, 'recall': 0.9383877159309021, 'f1-score': 0.9226922078841209, 'support': 20840}, '1': {'precision': 0.43230016313213704, 'recall': 0.10689794271883823, 'f1-score': 0.17141009055627424, 'support': 2479}, '2': {'precision': 0.8407502131287298, 'recall': 0.8705861581920904, 'f1-score': 0.8554081013097408, 'support': 28320}, '3': {'precision': 0.8391043397968606, 'recall': 0.8299086757990868, 'f1-score': 0.8344811753902663, 'support': 8760}, '4': {'precision': 0.7438380281690141, 'recall': 0.7647058823529411, 'f1-score': 0.7541276215975011, 'support': 8840}, 'accuracy': 0.8449862071953668, 'macro avg': {'precision': 0.7527011707767605, 'recall': 0.7020972749987717, 'f1-score': 0.7076238393475807, 'support': 69239}, 'weighted avg': {'precision': 0.8336396182945728, 'recall': 0.8449862071953668, 'f1-score': 0.8355916741609005, 'support': 69239}}\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "epoch 100 :finished\n",
      "train_loss: 0.39488842724205053\n",
      "val_loss: 0.42110823846788814\n",
      "{'0': {'precision': 0.9049028677150787, 'recall': 0.9387715930902112, 'f1-score': 0.9215261422515308, 'support': 20840}, '1': {'precision': 0.4093484419263456, 'recall': 0.11657926583299717, 'f1-score': 0.1814756671899529, 'support': 2479}, '2': {'precision': 0.8388170180978575, 'recall': 0.8723163841807909, 'f1-score': 0.85523878762701, 'support': 28320}, '3': {'precision': 0.8462174940898345, 'recall': 0.8172374429223744, 'f1-score': 0.8314750290360046, 'support': 8760}, '4': {'precision': 0.7518329260164408, 'recall': 0.7656108597285067, 'f1-score': 0.7586593431229682, 'support': 8840}, 'accuracy': 0.8446684671933448, 'macro avg': {'precision': 0.7502237495691114, 'recall': 0.7021031091509761, 'f1-score': 0.7096749938454933, 'support': 69239}, 'weighted avg': {'precision': 0.8331621891919477, 'recall': 0.8446684671933448, 'f1-score': 0.8357300841526474, 'support': 69239}}\n"
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    },
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     "output_type": "stream",
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      "100%|██████████| 100/100 [20:18:33<00:00, 731.13s/it]\n"
     ]
    }
   ],
   "source": [
    "EPOCH = 100\n",
    "loss_list=[]\n",
    "los_min=10**10\n",
    "val_loss_list=[]\n",
    "ac_list=[]\n",
    "\n",
    "for epoch in tqdm.tqdm(range(EPOCH)):\n",
    "    running_loss = 0.0\n",
    "    count=0\n",
    "    for _, (inputs, labels) in enumerate(train_dataloader, 0):\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        inputs1 = inputs.to(DEVICE)\n",
    "        labels = labels.to(DEVICE)\n",
    "        outputs = LSTMModel(inputs1)\n",
    "        \n",
    "        loss = criterion(outputs, labels.squeeze())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        count=count+1\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "    loss_loss=running_loss/count\n",
    "    loss_list.append(loss_loss)\n",
    "    print('epoch',epoch+1,':finished')\n",
    "    print('train_loss:',loss_loss)\n",
    "    with torch.no_grad():\n",
    "        count=0\n",
    "        running_loss=0.0\n",
    "        pre=list()\n",
    "        lab=list()\n",
    "        for _, (inputs, labels) in enumerate(test_dataloader, 0):\n",
    "            inputs1 = inputs.to(DEVICE)\n",
    "            labels=labels.to(DEVICE)\n",
    "            outputs = LSTMModel(inputs1)\n",
    "            loss =criterion(outputs, labels.squeeze())\n",
    "            running_loss += loss.item()\n",
    "            count+=1\n",
    "            _, predicted = torch.max(F.softmax(outputs).data, 1)\n",
    "            predicted=predicted.to('cpu')\n",
    "            labels=labels.to('cpu')\n",
    "            predicted=predicted.tolist()\n",
    "            labels=labels.tolist()\n",
    "            pre.append(predicted)\n",
    "            lab.append(labels)\n",
    "        loss_loss=running_loss/count\n",
    "        val_loss_list.append(loss_loss)\n",
    "        pre=sum(pre,[])\n",
    "        lab=sum(lab,[])\n",
    "        print('val_loss:',loss_loss)\n",
    "        cl = classification_report(lab, pre,output_dict=True)\n",
    "        print(cl)\n",
    "        ac_list.append(cl['accuracy'])\n",
    "#         if los_min>loss_loss:\n",
    "#             los_min=loss_loss\n",
    "#             torch.save(Transmodel.state_dict(),'Trans_8lay_state') \n",
    "\n",
    "        torch.save(LSTMModel.state_dict(),'/project/hikaku_db/ziwei/Model_12/Model_12_state_3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(loss_list[1:])\n",
    "plt.plot(val_loss_list[1:])\n",
    "plt.savefig('/project/hikaku_db/ziwei/Model_12/loss_plt_state_3.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ac_list)\n",
    "plt.savefig('/project/hikaku_db/ziwei/Model_12/ac_plt_state_3.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_list = pd.DataFrame([loss_list])\n",
    "val_loss_list = pd.DataFrame([val_loss_list])\n",
    "ac_list = pd.DataFrame([ac_list])\n",
    "loss_list.to_csv('/project/hikaku_db/ziwei/Model_12/loss_list_state_3.csv',header=False,index=False)\n",
    "val_loss_list.to_csv('/project/hikaku_db/ziwei/Model_12/val_loss_list_state_3.csv',header=False,index=False)\n",
    "ac_list.to_csv('/project/hikaku_db/ziwei/Model_12/ac_list_state_3.csv',header=False,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
